IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v22y1997i9p911-921.html
   My bibliography  Save this article

Modelling and forecasting monthly electric energy consumption in eastern Saudi Arabia using abductive networks

Author

Listed:
  • Abdel-Aal, R.E.
  • Al-Garni, A.Z.
  • Al-Nassar, Y.N.

Abstract

Abductive network machine learning is proposed as an alternative to the conventional multiple regression analysis method for modelling and forecasting monthly electric energy consumption. The AIM (abductory induction mechanism) is used to model the domestic consumption in the eastern province of Saudi Arabia in terms of key weather parameters and demographic and economy indicators. Models are synthesized by training on data for 5 years and forecasting new data for the sixth year. Compared to regression models previously developed for the same data, AIM models require fewer input parameters, are more accurate and are easier and faster to develop. An AIM model that uses only the mean relative humidity and air temperature gives an average forecasting error of about 5.6% over the year. Our study demonstrates the advantage of using actual values for monthly average weather data rather than means of such averages over a few years.

Suggested Citation

  • Abdel-Aal, R.E. & Al-Garni, A.Z. & Al-Nassar, Y.N., 1997. "Modelling and forecasting monthly electric energy consumption in eastern Saudi Arabia using abductive networks," Energy, Elsevier, vol. 22(9), pages 911-921.
    • Handle: RePEc:eee:energy:v:22:y:1997:i:9:p:911-921
    DOI: 10.1016/S0360-5442(97)00019-4
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544297000194
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/S0360-5442(97)00019-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:22:y:1997:i:9:p:911-921. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.